MICROBIAL OMICS FOR FOOD SAFETY - JHED Vol 6/02. FQS/11. Djuro Josic.pdf · MICROBIAL OMICS FOR FOOD SAFETY Djuro Josic1,2*, Dajana Gaso Sokac3, Martina Srajer Gajdosik3, ... protection

Journal of Hygienic Engineering and Design

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Review paperUDC 579.67

MICROBIAL OMICS FOR FOOD SAFETY

Djuro Josic1,2*, Dajana Gaso Sokac3, Martina Srajer Gajdosik3, James Clifton4

1Department of Biotechnology, University of Rijeka, Radmile Matejcic 2, 51000 Rijeka, Croatia and2Warren Alpert Medical School, Brown University, Providence RI, USA,

3Department of Chemistry, J. J. Strossmayer University, Cara Hadrijana 8/A, 31000 Osijek, Croatia,4Department of Pharmacology, Physiology and Biotechnology, Brown University,

Richmond Street 222, 02903 Providence, RI, USA

*e-mail: [email protected]

Abstract

Fungi, bacteria and other organisms secrete into the extracellular environment numerous compounds that are required for their survival. The secreted com-ponents can be scrutinized by proteomic and other “omic” tools. Many of these secretion systems are in-volved in pathogenic processes and indicate mecha-nisms of pathogenesis and could as well be of great interest for the applications in food technology and biotechnology. Further improvements in “omic” strat-egies and techniques will enable studies of pathogen secretomes in order to build data sets of proteins and other metabolites. Network of these components will lead to the increased understanding of interactions between the host and pathogen. The identification of proteins and small molecules that are produced by a still unknown pathogen will be the first step on the way of detection of food borne diseases.

We investigated the Gram positive Bacillus subtilis and Listeria monocytogenes, as well as the Gram negative Escherichia coli and Yersinia enterocolitica. Changes of proteome in these bacteria grown under stress con-ditions were identified. By the application of a new method for sample preparation, followed by LC–MS/MS, both characterization and comparison of pro-teomes of these food pathogens were achieved.

It was shown in all investigated bacteria that some of the proteins of key importance for protein turno-ver and metabolism are down regulated. Some stress proteins involved in protein folding and degradation were up regulated. Most of both up and down regulat-ed proteins belong to the group of proteins with high abundance. Flagellin is the only protein of lower abun-dance that is down regulated in two strains, B. subtilis and E. coli.

Presented results give better view into the proteome of food pathogens, and pave the way for further inves-tigation of their virulence, pathogenicity and detec-

tion of biomarkers for tracing the ways and sources of microbial food contamination.

Key words: Food microbial safety, Foodomics, Sample preparation, Mass spectrometry.

1. Introduction

Outbreak of food-borne diseases has always been a severe health risk in developing countries, but these diseases are also a problem in industrial countries. Microbial pathogens, mostly bacteria and fungi, are frequently responsible for both food spoilage and food-borne illnesses that cause enormous commercial and health damage around the world. Consequently, protection against spoilage and prevention of food-borne diseases is a task of enormous social and eco-nomic importance [1]. E.g. only in the U.S.A. each year are registered about 325,000 hospitalizations and 5,000 deaths that are caused by food poisoning [2].

In food production and storage, careful monitoring of microbial contamination in the final product as well as monitoring of the production process and cleaning and sanitation are one of the most essential factors of the manufacturing process. The tracing, identifica-tion and quantification of microbial contaminants and their toxins in food are important analytical problems. The most common bacteria that cause food poison-ing are Staphylococcus aureus, Campylobacter jejuni, some Salmonella, Staphylococcus and Bacillus spp. and Escheria coli, most frequently the O157:H7 strain, as well as toxin-producing fungi such as Aspergillus, Penicillium and Claviceps spp. [2-4]. There are well-es-tablished and sensitive methods for detection of food-borne pathogens and their toxins available, mostly based on immunochemical analyses. “Omics” technol-ogies, if applied in food analysis and technology are


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recently named “foodomics” [3]. These techniques are often more sensitive and specific and sensitive alterna-tives for identification of microbial food contaminants and their toxins, for monitoring of cleaning and san-itation, and for further validation of already existing

methods for analysis of food safety and quality [2-5]. Some of “foodomic” methods and strategies for mon-itoring of the quality and microbial safety of food of plant and animal origin are summarized in Table 1.

Table 1. Summary of on-line available data for use of “foodomic” technologies for monitoring of food quality and mi-crobial safety {according to Ref. [6] with permission}

Data types Online resource Description URL

Genomics Genomes OnLine Database (GOLD)

Repository of completed and ongoing genome projects http://www.genomesonline.org

Microbial Genome Database (MBGD)

MBGD is a database for comparative analysis of completely sequenced microbial genomes (ortholog identification, paralog clustering, motif analysis and gene order comparison)

http://mbgd.genome.ad.jp/

National Microbial Pathogen Data Resource (NMPDR)

The NMPDR provided curated annotations in an environment for comparative analysis of genomes and biological subsystems, with an emphasis on the food-borne pathogens

http://www.nmpdr.org/FIG/ wiki/view.cgi

Transcriptomics Gene Expression Omnibus (GEO)

Microarray and SAGE-based genome-wide expression profiles http://www.ncbi.nlm.nih.gov/geo

Stanford Microarray Database (SMD)

Microarray-based genome-wide expression data http://smd.princeton.edu/

ArrayExpress -functional genomics data

Functional genomics experiments include gene expression data from microarray and high throughput sequencing studies

http://www.ebi.ac.uk/ arrayexpress/

ExPASy – Bioinformatics Resource Portal

Links to transcriptomics http://www.expasy.org/ transcriptomics

Proteomics World-2DPAGE Links to 2D-PAGE data http://us.expasy.org/ch2d/2d-index.html


Link to protein sequences and identification

http://www.expasy.org/proteomics/protein_sequences_and_identification


Links to mass spectrometry and 2-DE data

http://www.expasy.org/proteomics/mass_spectrometry_and_2-DE_data

BIOBASE

BKL PROTEOME™ is a database and data analysis platform containing manually curated details from the PubMed literature in a highly structured and easily searchable format

http://www.proteinscience.com/databases.htm


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PRIDE – Proteomics Identifications Database

The PRIDE PRoteomics IDEntifications database is a centralized, standards compliant, public data repository for proteomics data, including protein and peptide identifications, post-translational modifications and supporting spectral evidence

http://www.ebi.ac.uk/pride/

Metabolomics MetaCycMetaCyc is a database of non-redundant, experimentally elucidated metabolic pathways

http://metacyc.org/

The Molecular Ancestry Network (MANET)

MANET database traces evolution of protein architecture onto biomolecular networks

http://www.manet.uiuc.edu/

Kyoto Encyclopedia of Genes and Genomes (KEGG)

KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large scale molecular datasets generated by genome sequencing and other high throughput experimental technologies

http://www.genome.jp/kegg/pathway.html

Metabolite and Tandem MS database (METLIN)

The METLIN Metabolite Database is a repository of metabolite information as well as tandem mass spectrometry data

http://metlin.scripps.edu/index.php

Lipidomics

Lipid Metabolites and Pathways Strategy (LIPID MAPS)

Genome-scale lipids database http://www.lipidmaps.org

The official database of Japanese Conference on the Biochemistry of Lipids (JCBL)

Lipid class database http://www.lipidbank.jp/

Avanti®,Polar Lipids,Inc.

Services for research and pharmaceutical scientists with the highest quality phospholipids, sphingolipids and sterols

http://avantilipids.com

The AOCS Lipid Library The AOCS Lipid Library http://lipidlibrary.aocs.org/

index.html

Cyberlipid Lipid databases and encyclopedia http://www.cyberlipid.org

LipidHomeLipidHome theoretically generates lipid molecules and useful metadata

http://www.ebi.ac.uk/apweilersrv/lipidhome

* This table presents some of the databases that store and distribute omics data sets through publicly accessible Web sites. Some omics tech-nologies such as glycomics do not yet have associated data-dissemination resources, and are therefore not included.


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2. Use of omics methods for identification of microbial contaminants

Sensitive routine methods are available for identifica-tion of microbial contaminants. They are mostly based on immunochemical techniques for detection of mi-crobial antigens and their products, mostly secreted metabolites. Omics technologies, mostly proteomics, genomics, glycomics, lipidomics and metabolomics are further, more sensitive and specific ways for iden-tification of microbial food contaminants and their toxins, and for monitoring of cleaning and sanitation [2, 3]. Among others, DNA microarray technology, GC-MS based metabolomics, LC-MS/MS based proteomics and lipidomic methods were applied [3, 7 - 9]. To follow changes during food processing, omics investigations of model microorganisms such as both bacteria and fungi under stress conditions such as cold and heat in-fluence, osmotic pressure, high pressure, availability of nutrients and other environmental factors are essen-tial in order to follow their adaptation and reaction to extreme conditions [7]. Their adaptive stress response is a crucial mode of cellular protection towards envi-ronmental and food relevant stress. Cellular and meta-bolic biomarkers are correlating to the adaptive stress and can also predict the impact of the environmental changing on microbial ability to resistance and surviv-al. Minimally processed, so called “ready to eat” food that have become very popular in recent years, and antimicrobial washing by using different disinfectants and natural antimicrobial agents are other important factors that drastically change populations of microor-ganisms in fresh food. Such food can still retain some potential pathogenic bacteria, such as Aeromonas spp. and Yersinia spp. and even some unexpected contam-inants [10]. It is also documented that the recent se-rious outbreak of food poisoning in Germany, caused by a novel strain of E. coli appeared after consuming of such minimally processed food [4, 11].

2.1. Use of omics for following the microbial stress adaptation

The adaptive stress response of microbial population is a crucial mode of cellular protection towards environ-mental and food-relevant stresses. Some cellular com-ponents, mostly specific proteins and metabolites, are quantitatively correlating to adaptive stress and can also predict the impact of changing environments on microbial ability to resistance and survival [11]. Several molecular biomarkers for stress adaptive behavior at transcriptome, proteome and metabolome level were already identified. In Bacillus cereus as a model micro-organism, potential candidate biomarkers to stress response were following proteins: transcriptional reg-ulator sB, catalases involved in H2O2-scavenging, chap-erones and ATP-dependent Clp proteases involved in protein repair and maintenance [12]. In an overview,

Abee et al. [13] integrated three different «omics» strat-egies, namely use of information from transcriptomic and proteomic data, as well as determination of activ-ity of marker enzymes. This approach has led to the identification of biomarkers important for prediction of the robustness level of adaption of the microorgan-ism towards the lethal stresses.

The investigations using a wide variety of environ-mental changes were extended to the broad range of food-borne contaminants [13-16]. Such quantitative approaches by use of several omics methods lead to prediction of microbial performance using molecular biomarkers for the early detection of food pathogens and to the control of their adaptive behavior that re-sults in enhanced resistance [4, 11-16]. The strategy for use of «omics» method in order to enhance food quality and safety is presented in Figure 1 [17]. Most frequently investigated were changes after:1. Temperature shock (heating or freezing, References

[18] and [19]) 2. Osmotic stress and high hydrostatic pressure [18, 20]3. High hydrostatic pressure [20]4. Other stress factors such as oxidizing agents and

other disinfectants [16, 21].

Figure 1. Use of foodomics in the development pathway for food production, and assessing food safety, origin

and quality. {Adapted from [2], with permission}

2.2. Food toxins

Food toxins are cellular components or metabolic products of food pathogens, and they are severe treat to human health. Bacterial and fungal toxins (mycotox-ins) may be acutely toxic, but especially the mycotoxins may also cause chronic damage, such as teratogenic, immunotoxic, nephrotoxic and estrogenic effects [11]. Most intracellular and secreted bacterial toxins that cause food poisoning are intensively studied together with bacterial food poisoning, and they are discussed above.

In addition, there have been considerable public safety concerns, thus interest for the investigation of fungal


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toxins, toxic chemicals in food and possible changes during the food processing [22]. These events may contaminate the soil, water and air with mycotoxins. Sometimes it is difficult to link poisonings caused by toxic components to a particular food; the onset of the effects may be gradual and not be detected until chronic or permanent damage occurs. Surveillance studies showed that mycotoxin contamination is a worldwide problem, especially in developing coun-tries, where suitable cultivation, processing and stor-age technologies are implemented with difficulty [23]. Identification of biomarkers of the most-relevant toxins that have been detected in this type of environ-ment and their monitoring will yield a more accurate risk assessment.

Around 400 mycotoxins have been registered until to-day. They can be found in a variety of foods, ranging from cereals, peanuts, spices, fruits and vegetables. In most cases the mycotoxins that are found in products of animal origin, mostly in meat, milk and eggs are originated from animal feeds.

The classes of mycotoxins are aflatoxins, ochratoxins, trichtothecenes, zearalenone, fumonisins, tremorgen-ic toxins and ergot alkaloids. After suitable sample preparation, even low concentration of mycotoxins can be detected by use of metabolomic techniques [11, 17, and 23].

New safety risks have to be taken into consideration because of the continuous adaptation of the relevant food borne pathogens and other potentially pathogen microorganisms, changing production methodolo-gies, changes in the environment and increase of glob-al trade of foodstuffs. Increased demand for traditional food in industrial countries, and with growing living standard in some developing countries has to be taken into consideration [11, 13].

2.3 Model system - inhibitory activity of pyridinium oximes to Gram-positive and Gram-negative food pathogens

Pyridinium oximes have been firstly demonstrated to be potent re-activators of organophosphate-inhibit-ed enzyme acetylcholine esterase (AChE). Pyridoxal oxime, a derivative of vitamin B6 can be used for syn-thesis of compounds structurally similar to common antidotes, and their derivatives have been tested as re-activators of AChE after inhibition of neural poisons [24]. Quaternary ammonium salts of heterocyclic bases of dimethylpyridine (see Figures 2 and 3) have been al-ready evaluated for antimicrobial activity against both Gram-positive and Gram-negative microorganisms [16]. Quaternary salts bind to the cell wall as well as other cellular compounds of the microorganisms caus-ing inhibition of bacterial growth and cell death [25]. These activities make them suitable for use as model substances for proteomic and other “omic” studies to find changes in both cell surface and intracellular pro-teins of four investigated food borne pathogens, Gram-positive bacteria Bacillus subtilis and Listeria monocy-togenes, as well as Gram-negative bacteria Escherichia coli and Yersinia enterocolitica.

Methods for cellular destruction, fractionation and identification of selectively solubilized proteins that were separated according to their hydrophobicity were developed. In the next step, proteins were iden-tified by semi-quantitative LC-ESI-MS/MS, and changes in the bacterial proteomes were investigated.

Figure 2. Synthesis of the oximes 2 and 3 from pyridoxal oxime


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2.3.1 Results

As shown in Figures 4 A and B, destruction of bacteri-al cells and proper extraction of proteins according to their hydrophobicity are the key steps in the sample preparation before their identification by LC-MS/MS. Sonication alone is not sufficient as a method for cell destruction, additional mechanical treatment is neces-sary for optimal disintegration and optimal extraction of cellular components.

In the first step, gel bands of interest after SDS-PAGE (see Figure 5) were excised and subjected to tryptic di-gestion as previously described [26]. The proteins were subsequently identified by LC-ESI-MS/MS [16].

Figure 3. Anion exchange reaction (adapted from Ref. [16] with permission)

Figure 4. A - SDS-PAGE of cell extracts of Gram positive bacteria obtained after sonication (only) and subsequent extraction with lysis buffer. Lanes 1-5 - Bacillus subtilis, 6-9 - Listeria monocytogenes; B - SDS -PAGE of cell extracts of

Gram positive bacteria obtained after sonication, mechan-ical destruction of cells and subsequent extraction with

lysis buffer. Lanes 1-5 - Bacillus subtilis, 6-9 - Listeria mono-cytogenes (Adapted from Reference [16] with permission)

A

B

Figure 5. SDS-PAGE of cell extracts of Gram negative bacteria obtained after sonication (only) and

subsequent extraction with lysis buffer. Lanes 1-6 - Escherichia coli, 6-9 - Yersinia enterocolitica. Differently expressed proteins were recognized according to the band intensity (see band numbers), excised, digested

with trypsin and identified by LC–MS/MS


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Especially for Gram positive bacteria (B. subtilis and L. monocytogenes) this treatment was not sufficient for complete cell destruction (see Figure 4A), and before sonication, the cells had to be destroyed mechanical-ly with micro glass beads in order to obtain sufficient amount of extracted proteins (see Figure 4B).

In order to trace differently expressed proteins, we separated 40 samples of four bacteria treated with pyridinium oximes and control samples of untreated bacteria. After SDS-PAGE, up- and down-expressed proteins were recognized as bands of different intensi-ty (see Figure 5). They were excised, extracted, trypsin-ized and identified by LC-MS/MS. The lists of differently expressed proteins are shown in Tables 2-5. In these tables, the names of proteins that are up or down-reg-ulated in more than one bacterium are in bold.

Table 2. The list of differently expressed proteins after treatment with pyridinium oximes in Bacillus subtilis

Table 3. The list of differently expressed proteins after treatment with pyridinium oximes in Escherichia coli

For the “in-solution” digestion extracted proteins were precipitated, digested with trypsin and identified by LC-MS/MS. In each of analyzed three fractions that were selectively extracted, between 400 and 600 proteins

were identified, and altogether, in bacteria more than 1800 proteins each were identified. In Figure 6, the numbers of down and up-regulated proteins in each bacterium after treatment with pyridinium oximes are shown. These inhibitors seem to be mostly effec-tive in B. subtilis cell, and in Y. enterocolitica the lowest number of proteins were up or down-regulated. The lists of twenty most abundant proteins that are up or down-regulated in each of investigated bacteria are shown in supplement, Tables S1 - S1.

Table 4. The list of differently expressed proteins after treatment with pyridinium oximes in Yersinia enterocolitica

Table 5. The list of differently expressed proteins after treat-ment with pyridinium oximes in Listeria monocytogenes

2.4 Discussion

These investigations of four model bacteria clear-ly show that destruction of Gram-positive bacteria for proteomic investigation is a pretty difficult task. Therefore, the detection of intracellular proteins com-ing from this type of food-borne pathogens will be practically impossible in most of the cases. The inves-tigation should be focused instead on detection of secreted proteins and other extracellular components.

Here, we investigated changes resulting from chemical insult, specifically by pyridinium oximes. The twenty


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most abundant proteins that are up or down-regulat-ed in each of the investigated bacteria (see Tables 1S-8S) give a representative overview about the changes in each microorganism. As shown in Figure 6, B. subtilis and E. coli are mostly prone to proteome changes after inhibition, and Y. enterocolitica is a less sensitive micro-organism.

Proteins that are differently expressed in more than 1 bacterium are summarized in Table 6. Most of them are enzymes and co-factors involve energy metabo-lism, heat shock proteins and some membrane- and cell wall associated proteins (see also Tables S1-S8). A change of expression of proteins that are involved in energy metabolism and protein synthesis could be ex-pected because all the investigated substances were added in concentrations that inhibit bacterial growth.

As expected, the influence of inhibitory agents also causes induction of the expression of stress proteins in the cell. Chaperon GroEL, e.g., a heat shock protein, is up-regulated in all four bacteria (see Tables 2-6).

Flagellin was the only protein with lower abundance that was down-regulated in both B. subtilis and E. coli in the presence of inhibitory agents (see Table 6). Down-regulation of this protein may significantly reduce bac-terial virulence. On the other hand, this protein is also an important antigen that is frequently recognized by the human immune system, and anti-flagellin antibod-ies are used as non-specific protection against bacteri-al pathogens [27].

Table 6. Summary of proteins that are up or down regulated in more than one bacterium, and detected after “in-gel”. The names of these proteins are in bold in Tables 2 to 5

3. Conclusions- Application of the presented method for sample preparation follow by in-gel and in-solution digestion and Lc-MS/MS enables the investigation and quantita-tive comparison of both Gram negative and Gram pos-itive food pathogens.

- Destruction of Gram-positive bacteria requests an ad-ditional, mechanical step, and it is an indication that detection of their intracellular proteins in samples of contaminated food may be difficult.

- Following the fact stressed above, in the case of Gram-positive bacterial contaminants their detection shall be performed through detection of extracellular components (secretome).

Figure 6. Number of down- and up-regulated proteins in model microorganismsafter treatment with pyridinium oximes


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- Furthermore, it was shown that in all investigated food borne pathogens many proteins of key impor-tance for protein turnover are down-regulated after incubation in the presence of pyridinium oximes.

- Some stress proteins involved in protein folding and degradation are up-regulated.

- Flagellin is the only low abundance protein that was found to be down regulated in two strains, the Gram positive bacterium B. subtilis and the Gram negative one E. coli.

- Flagellin is a protein that plays an important role in bacterial virulence and induction of host immunity [28].

- The Gram positive bacterium B. subtilis is the mostly sensitive to changes induced by applied inhibitors.

- The changes in Gram negative bacterium Y. enteroco-litica proteome induced by pyridinium oximes are low-est comparing to other investigated bacteria.

Supplement Table S1. Bacillus subtilis - down regulated proteins

Protein Name Total Score Total Peptides

1. Lon protease 1 1086,44 23

2. Ribose-phosphate pyrophosphokinase 993,67 16

3. Glycyl-tRNA synthetase beta subunit 952,82 21

4. DNA polymerase III subunit beta 941,92 18

5. NADH dehydrogenase 808,26 17

6. Peptide chain release factor 1 760,48 15

7. D-alanine--D-alanine ligase 743,61 13

8. Methionyl-tRNA synthetase 741,86 15

9. 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase 731,78 12

10. Transcription elongation protein nusA 713,82 13

11. Aspartyl-tRNA synthetase 704,44 14

12. Sensor protein degS 636,04 12

13. DNA mismatch repair protein mutS 604,1 13

14. DNA polymerase I 601,25 15

15. UDP-N-acetylmuramoyl-L-alanyl-D-glutamate-2,6-diaminopimelate ligase 600,67 10

16. UPF0042 nucleotide-binding protein yvcJ 570,97 10

17. Lipoate-protein ligase LplJ 568,67 12

18. Enoyl-[acyl-carrier-protein] reductase [NADPH] FabL 565,93 11

19. Uncharacterized protein ydcI 553,24 14

20. Ferrochelatase 459,2 12

Table S2. Bacillus subtilis - up regulated proteins


1. Penicillin-binding protein 1A/1B 524,66 10

2. Uncharacterized protein yjlC 430,95 7

3. L-cystine-binding protein tcyA 287,34 5

4. Penicillin-binding protein 3 283,03 7

5. Disulfide bond formation protein D 226,66 5


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6. Manganese-binding lipoprotein mntA 225,33 5

7. Oligopeptide-binding protein oppA 215,17 5

8. D-alanyl-D-alanine carboxypeptidase dacA 205,95 6

9. L-lactate permease 184,05 3

10. Uncharacterized protein yrhD 177,94 4

11. Membrane protein oxaA 1 175,02 3

12. Cell cycle protein gpsB 169,54 4

13. ATP synthase epsilon chain 166,14 5

14. Methionine-binding lipoprotein metQ 163,59 4

15. Uncharacterized membrane protein yhaH 163,04 5

16. Dihydrolipoyl dehydrogenase 148,53 4

17. L-cystine transport system permease protein tcyB 148,21 3

18. 50S ribosomal protein L29 143,54 3

19. Phage-like element PBSX protein xkdF 137,79 3

20. Putative carboxypeptidase yodJ 135,07 4

Table S3. Listeria monocytogenes - down regulated proteins


1. aconitate hydratase 740,16 16

2. ribonucleotide-diphosphate reductase subunit alpha 659,96 13

3. hypothetical protein LM5578_0641 637,50 12

4. NAD-dependent DNA ligase LigA 577,53 12






10. phosphoribosylaminoimidazole carboxylase ATPase subunit 366,66 4

11. glutamate-1-semialdehyde aminotransferase 347,05 8




15. ribonuclease PH 290,09 5

16. azoreductase 284,02 5



19. two-component response regulator 221,27 5

20. prephenate dehydratase 218,56 4


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Table S4. Listeria monocytogenes - up regulated proteins


1. listeriolysin O precursor 918,80 19

2. pyridoxine biosynthesis protein 466,85 9

3. leucyl-tRNA synthetase 308,80 8


5. transmembrane protein 282,99 5











16. glutamate dehydrogenase 143,91 4




20. LacI family transcription regulator 97,56 3

Table S5. Escherichia coli - down regulated proteins


1. alcohol dehydrogenase (adhE) 3081,22 50

2. trimethylamine N-oxide reductase subunit 871,38 19

3. IrgA-like protein 741,08 14

4. DkgA 725,40 16

5. Chain A, E. Coli Ferric Hydroxamate Uptake Receptor (Fhua) In Complex With Bound Ferrichrome-Iron 644,45 14

6. trimethylamine-N-oxide reductase 623,75 14

7. Chain A, Crystal Structure Of N-Acetyl-D-Glucosamine-6-Phosphate Deacetylase Liganded With Zn 511,82 10

8. glyceraldehyde 3-phosphate dehydrogenase C 446,76 7

9. outer membrane protein C OmpC 439,61 9

10. osmC 386,76 7

11. pyruvate oxidase 377,26 8

12. Chain C, The Crystal Structure Of Unliganded Phosphofructokinase 376,18 8


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13. hypothetical protein ECs2150 372,05 7

14. Chain B, Complex Of Enzyme Iiaglc And The Histidine-Containing Phosphocarrier Protein Hpr 360,57 5

15. Chain A, Phosphate-Binding Protein With Ala 197 Replaced With Trp 354,82 7

16. ABC transport system, periplasmic binding protein SitA 311,86 8

17. SitA 304,64 8

18. cold shock protein (cspA) 288,23 7

19. threonine dehydratase 2 (EC 4.2.1.16) 285,55 5

20. Mannonate dehydratase 276,11 7

Table S6. Escherichia coli - up regulated proteins


1. type-1 fimbrial major subunit 416,04 5

2. type 1 fimbrin 365,26 5

3. phoP 332,27 7

4. NADH dehydrogenase I chain G 329,67 8

5. tryptophanase 320,73 7

6. carbamoyl-phosphate synthetase small subunit 304,91 6

7. unnamed protein product 265,08 8

8. hypoxanthine phosphoribosyltransferase 251,99 5


10. Chain B, Structural Genomics, Protein Ybgi 169,36 4

11. Chain A, Thymidylate Synthase Complexed With Dgmp And Folate Analog 1843u89 164,66 3


13. heat shock protein IbpA 146,49 3


15. hycF gene 145,39 3

16. putative ATP synthase beta subunit 134,05 3

17. uracil DNA glycosylase 133,76 3


19. phosphoglycolate phosphatase 103,31 2

20. cysB regulatory protein 95,40 3

Table S7. Yersinia enterocolitica - down regulated proteins


1. N-ethylmaleimide reductase 1023,73 19

2. threonine synthase 611,50 12

3. ATP-dependent RNA helicase DeaD 553,06 13

4. hypothetical protein YE2259 503,17 11


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5. tRNA modification GTPase TrmE 480,77 9

6. cyclopropane fatty acyl phospholipid synthase 471,52 11

7. arginine-binding periplasmic protein 1 precursor 401,75 9

8. putative D-isomer specific 2-hydroxyacid dehydrogenase 379,07 7

9. alcohol dehydrogenase 369,46 7

10. 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase 363,82 7

11. thymidine phosphorylase 351,34 9

12. adenylosuccinate lyase 344,78 8

13. dihydroorotate dehydrogenase 2 330,40 6

14. putative global regulator 325,74 5

15. thioredoxin-dependent thiol peroxidase 312,29 5

16. ubiquinone/menaquinone biosynthesis methyltransferase 308,94 6

17. flavodoxin FldA 299,91 6

18. azoreductase 298,30 6


20. fumarate reductase flavoprotein subunit 277,25 7

Table S8. Yersinia enterocolitica - up regulated proteins


1. 30S ribosomal protein S17 335,39 9

2. putative arsenate reductase 315,04 6

3. 30S ribosomal protein S15 276,14 8

4. leucyl aminopeptidase 228,15 5

5. 1-(5-phosphoribosyl)-5-[(5-phosphoribosylamino)methylideneamino] imidazole-4-carboxamide isomerase 216,83 5

6. peptidyl-prolyl cis-trans isomerase C 205,92 5

7. putative ribosome maturation factor 186,16 5


9. aspartate alpha-decarboxylase 159,98 3

10. DNA-binding transcriptional repressor PurR 144,82 3

11. pyrrolidone-carboxylate peptidase 141,47 4

12. malic enzyme 139,44 4

13. formyltetrahydrofolate deformylase 131,05 4


15. aromatic amino acid aminotransferase 125,62 3

16. Maf-like protein 120,62 3

17. glycine cleavage system aminomethyltransferase T 117,54 2

18. long-chain fatty acid outer membrane transporter 110,68 2

19. 23S rRNA pseudouridine synthase D 101,09 2

20. DNA-binding transcriptional regulator HexR 98,78 2


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MICROBIAL OMICS FOR FOOD SAFETY - JHED Vol 6/02. FQS/11. Djuro Josic.pdf · MICROBIAL OMICS FOR FOOD SAFETY Djuro Josic1,2*, Dajana Gaso Sokac3, Martina Srajer Gajdosik3, ... protection